Sentence Similarity
sentence-transformers
Safetensors
English
nomic_bert
feature-extraction
Generated from Trainer
dataset_size:35934
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use KaiserML/RhetoriBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KaiserML/RhetoriBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KaiserML/RhetoriBERT", trust_remote_code=True) sentences = [ "Stating purpose of the current research with reference to gaps or issues in the literature", "During the 15-year study, 10% of the osseointegrated implants in the edentulous jaw showed signs of peri-implantitis, leading to their failure.", "This paper provides an in-depth exploration of the qualitative case study methodology, addressing the lack of comprehensive guidance for novice researchers in this area.", "As a novice researcher in management science, I have been drawn to the qualitative case study methodology due to its ability to provide rich, in-depth insights into complex real-world situations." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle